Bowling et al. BMC Psychology (2016) 4:58
DOI 10.1186/s40359-016-0164-x

RESEARCH ARTICLE

Open Access

Is mid-life social participation associated
with cognitive function at age 50? Results
from the British National Child
Development Study (NCDS)
Ann Bowling1* , Jitka Pikhartova1,2 and Brian Dodgeon3

Abstract
Background: Some studies have indicated that social engagement is associated with better cognitive outcomes.
This study aimed to investigate associations between life-course social engagement (civic participation)
and cognitive status at age 50, adjusting for social networks and support, behavioural, health, social and
socio-economic characteristics.
Methods: The vehicle for the study was the National Child Development Study (1958 Birth Cohort Study),
which is a general population sample in England, Scotland and Wales (9119: 4497 men and 4622 women)
participating in nationally representative, prospective birth cohort surveys. The primary outcome variable
was cognitive status at age 50, measured by memory test (immediate and delayed word recall test) and
executive functioning test (word fluency and letter cancelation tests). The influence of hypothesised
predictor variables was analysed using linear multiple regression analysis.
Results: Cognitive ability at age 11 (β = 0.19;95% CI = 0.17 to 0.21), participation in civic activities at ages 33
(0.12; 0.02 to 0.22) and 50 (0.13; 0.07 to 0.20), frequent engagement in physical activity (sport) (β from
0.15 to 0.18), achieving higher level qualifications (β from 0.23 to 1.08), and female gender (β = 0.49;95% CI
= 0.38 to 0.60) were positively, significantly and independently associated with cognitive status at age
50. Having low socio-economic status at ages 11 (β from -0.22 to -0.27) and 42 (β from -0.28 to -0.38), and
manifesting worse mental well-being at age 42 (β = -0.18; 95% CI = -0.33 to -0.02) were inversely associated
with cognitive status at age 50. The proportion of explained variance in the multiple regression model

(18%), while modest, is impressive given the multi-faceted causal nature of cognitive status.
Conclusions: The results indicate that modest associations between adult social engagement and cognitive
function at age 50 persist after adjusting for covariates which included health, socio-economic status and
gender, supporting theories of neuroplasticity. In addition to the continuing emphasis on physical activity,
the encouragement of civic participation, at least as early as mid-life, should be a targeted policy to
potentially promote and protect cognitive function in later mid-life.
Keywords: Cognitive function, Civic engagement, Predictor, Longitudinal, Cohort

* Correspondence:
1
Faculty of Health Sciences, University of Southampton, Highfield Campus,
Southampton SO171BJ, UK
Full list of author information is available at the end of the article
© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License ( which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
( applies to the data made available in this article, unless otherwise stated.


Bowling et al. BMC Psychology (2016) 4:58

Background
Decline in cognitive and physical functioning is likely to
reflect interactions between a person’s genes, biology,
socio-economic and environmental circumstances, behaviour, socio-psychological and physical reserves [1].
Even with similar neurodegenerative changes, individuals
vary considerably in their severity of cognitive aging [2].
Understanding potential interactions between social and
biological processes, using a life-course perspective, is

important to advancing potential causal explanations of
disease onset and progression.
Vascular disease has been reported to be associated with
cognitive impairment [3], as has having no leisure activities, childhood adversity, being in a lower socio-economic
group, having less education, lower intelligence test scores,
smoking, being female and older age [2, 4–13]. Relations
between cognitive function and education [14, 15], as well
as gender [16, 17] and alcohol use [18, 19], are not conclusive. For example, while education is associated with
cognitive function, it is not always associated with rate of
cognitive decline [15]. Longitudinal analyses have also
indicated that those with different levels of education have
similar brain pathology, but those with more years of
education are better able to compensate for the effects of
dementia [13].
Research across disciplines has indicated that physical
activity is associated with lower risks of cognitive impairment [20–22]. Physical activity sustains cerebral blood
flow by decreasing blood pressure, lowering lipid levels,
inhibiting platelet aggregation or enhancing metabolic
demands, and may improve aerobic capacity and cerebral
nutrient supply [20]. However, engaging in physical activity is a marker of better health status, itself associated with
lower risk of cognitive impairment and dementia.
Potential health protectors include social support (interactive processes whereby emotional, instrumental or
financial aid is obtained from social network members)
and the distinct concepts of civic engagement (ways in
which people participate in their communities to improve
lives or shape the community) and social capital (opportunities within communities to increase social resources
through involvement in social, leisure, recreational activities, voluntary work, group membership, political activism, education) [23–25]. A small number of surveys have
indicated that social integration, social engagement, and
having strong networks are associated with better cognitive outcomes [26, 27] along with social and physical
participation [6, 28]. For example, Fratiglioni et al. [26]

combined four social network variables into an index, and
reported that a poor or limited social network significantly
increased the risk of dementia, with a significant gradient
found for the four degrees of social connections. Read and
Grundy [29] analysed data from the English Longitudinal
Study of Ageing and reported poorer cognition in childless

Page 2 of 15

people, suggesting that there may be benefits to cognitive
function from rearing and nurturing children. SinghManoux et al. [30], in cross-sectional analyses of phase 5
of the Whitehall II study, reported that, controlling for
socio-economic status, participation in cognitively complex or socially oriented leisure activities had independent
associations with cognitive status in middle age groups.
Activities high on social engagement had a stronger and
more consistent association with cognition than individual
leisure activities. Singh-Manoux et al referred to other
research indicating that active leisure is associated with
adult cognition after adjusting for previously measured
cognitive status [6].
Despite heterogeneity in study design and measures, a
systematic review of the literature on social relationships
and cognitive decline reported meta-analyses which
showed that multiple aspects of social relationships are
associated with cognitive decline [31]. In relation to such
associations, the concept of a ‘mental bank’ has been
coined, which can be increased or decreased by life
experiences, and includes cognitive and affective resources (skills cognitive flexibility, effectiveness in learning, intelligence emotional or social skills and resistance
to stress) [32]. These studies indicate the types of public
health interventions that might improve cognitive

health. Beddington et al. [33] argued, countries must
learn how to capitalize on their citizens' cognitive resources if they are to prosper, both economically and socially, and suggested that early interventions will be key.
Theoretical frameworks for causal mechanisms include
the effects of social and mentally stimulating interaction
and participation, which may preserve cognitive function
via activating thinking and attention [34]. This theory
allows for people with higher cognitive reserve to avoid
showing symptoms of cognitive decline for longer periods than those with lower cognitive reserve [13]. Social
interaction requires many behaviours requiring cognitive
skills (memory, attention, control) [35].
Social relationships may also provide stress buffering
resources via the provision of informational, emotional,
tangible and companionship support, by facilitating connectivity within the social network, and enhancing social
integration [36]. Social relationships may also facilitate
participation in social and other activities, thereby enhancing a self-concept of usefulness, of having a social
role in life, self-esteem and identity, and maintainings a
sense of self-efficacy, as well as provision of information
(e.g. about health) [37, 38]. Participation in productive,
civic or social activities may enhance one's self-concept
of being useful, thereby increasing or maintaining selfesteem, identity, and self-efficacy. Szreter and Woolcock
[39] pointed to the vast amount of research indicating
that social capital is linked to enhanced well-being,
reported mental and physical health, positive health


Bowling et al. BMC Psychology (2016) 4:58

behaviours, reduced levels of stress, loneliness and isolation. Such social resources have long been hypothesised
to directly or indirectly promote a person’s adaptive behavioural responses to stress [40]. In relation to biomedical pathways, Lacey et al. [41] reported an association
between social isolation and stress biomarkers (C-reactive protein). However, the literature also indicates that

certain lifestyle factors which might be expected to
increase cortisol secretion actually lead to a levelling of
cortisol levels, suggesting that cortisol is less indicative
of stress than expected, and that other stress biomarkers
(including fibrinogen) may have a role [42, 43]. The need
to examine associations between social resources and
cognitive function further, and using a life-course approach, led to the study reported here.

Aim
The aim of this study was to investigate the influence of
life-course indicators of social engagement in civic activities
on cognitive status at age 50, controlling for potential influences of early-life cognition (age 11), social networks and
support, physical and mental health, health behaviours,
socio-demographic and socio-economic characteristics.

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Sample

Sample members who completed NCDS surveys at both
ages 11 and 50 were eligible for inclusion in the analyses
reported here (n = 9119). Of these, 8129 (89.1%) completed the cognitive tests at both ages. Their survey data
collected at ages 11, 33, 42 and 50 was analysed and is
presented here. Cognitive results were imputed for 990
individuals (for age 11 or 50, or both); all 9119 were
included in the analysis.
Age 11 was selected because the range of cognitive
tests was wider. General Cognitive Ability was assessed
at age 11 and not age 16, and most of those who were
present in the study at 50 were present also at age 11,

optimising the sample size for analysis (14,126 cohort
members completed the age 11 tests, but far fewer,
11,920, completed the age 16 English and maths tests).
Age 11 cognitive tests also feature prominently in the literature [46–52]. Ages 33, 42 and 50 were selected for
analysis because these were the principal adult survey
sweeps of NCDS (i.e. NCDS5, NCDS6 and NCDS8), and
questions were included which measured the variables
of interest here.
Measures

Methods
Study data

The study used data from the British National Child
Development Study (NCDS), a prospective cohort study
originating in the Perinatal Mortality Survey [44]. The
latter examined social and obstetric factors associated with
still birth and infant mortality among over 17,400 babies
born in Britain in 1 week in March 1958. Surviving
members of this birth cohort were followed up on nine
occasions in order to monitor changes in health, education, social and economic circumstances. The follow-ups
were in 1965 (age 7), 1969 (age 11), 1974 (age 16), 1981
(age 23), 1991 (age 33), 1999/2000 (age 41/2), 2004–2005
(age 46/47), 2008–2009 (age 50), and a sequential
mixed-methods follow-up in 2013 (age 55). Data about
educational development, health behaviours, physical development, well-being, family life, economic circumstances,
employment, social participation and attitudes towards life
were collected. There have also been sub-sample surveys of the cohort. For example, participants were
contacted at age 20 to map their examination achievements; and at age 44 to collect biomedical markers. Further information about the NCDS can be found on the
Centre for Longitudinal Studies website (www.cls.ioe.ac

.uk/ncds). Data for the NCDS sweeps are accessible
( The initial response rate
to NCDS was just over 98% of all births in Great Britain
in that week; although responses to subsequent waves varied (see Additional file 1). Power and Elliot [45] described
respondent profiles.

Cognitive status at age 50 was the dependent variable,
measured with memory and executive functioning tests,
which have been widely used in surveys, and well tested
[4, 53]. Memory was assessed by a word-recall test,
involving memorising words with immediate and delayed recall. Respondents could score between 0 and 10
in both immediate and delayed recall tests, reflecting
number of words remembered (thus higher scores
reflected better performance). The overall score is calculated as sum of both recall tests, ranging between 0 and
20. Executive functioning was measured by letter
cancellation and naming tests. Naming as many words
from a particular category was used to test verbal fluency, and letter cancellation was used to test visual
attention, speed and concentration. Respondents were
asked to name as many animals as possible within one
minute. In the letter cancellation test, respondents were
asked to cross as many P’s and W’s as they could spot in
the list of letters within one minute (maximum: 69);
letter accuracy is the number of letters missed in the
text during a test, with a lower score equating with a
better result (the polarity was reversed to enable summing the standardised scores). Each test score was
standardised to allow comparisons between all tests, and
overall cognitive score was calculated by summing the
standardised scores from each individual test.
Independent variables were selected according to their
theoretical importance in the literature and comparable

questions being repeated between waves. The influence of
civic engagement and social activities on cognitive status at


Bowling et al. BMC Psychology (2016) 4:58

age 50 was examined by number and type of civic group activities currently participated in (ages 33, 50): membership
of political party, trade union, environmental group, parents
school association, residential group and neighbourhood
watch, religious group or church organization, voluntary
service group, other community, civic group, social/working men’s club, sports club, women’s institute/Townswomen’s Guild, women’s group, feminist organisation,
professional organization, pensioners group/organization
(actual question wording), scouts/guides organization, or
others) formed a derived variable about civic engagement.
In addition, other social activities measured at age 50 included visits to theatres, concerts, cinema, live sport events
or pub/restaurant. A variable was created to represent the
total number of civic activities engaged in by respondents
at certain ages. This was derived at age 33 using reported
numbers of civic activities engaged in (political party, charity/environmental groups, school/parental organizations,
neighbourhood/residents associations, and women’s institutes/groups); and age 50 respondents were asked separately for each type of civic activity and positive answers
were then summed to provide the total number of civic activities engaged in.
The independent variables analysed as potential confounders included early-life cognition (age 11), social
networks and support, physical and mental health,
health behaviours, socio-demographic and socio-economic
characteristics:
Cognitive ability at age 11: Cognitive tests at age 11 were
used to measure child cognitive ability: reading, mathematics, copying designs and general ability. The Reading
Comprehension Test had scores between 0 and 35, Arithmetic/Mathematics Test between 0 and 40, Copying Design Test (in which children copied 6 objects, each twice)
between 0 and 12 and General Ability Test (consisting of
40 verbal and non-verbal tasks, tested by their teachers,

designed by the National Foundation for Educational Research [54] between 0 and 80. As with cognition at age 50,
each score was standardised to allow comparison between
tests, and overall cognitive score at age 11 was derived by
summing the standardised scores of all four tests. For cognition at age 11 and at age 50, categorical variables were
also constructed by dividing standardised continuous
scores using cut-offs of –0.5 S.D. and +0.5 S.D. and creating ‘below mean’, ‘mean’ and ‘above mean’ categories of
cognitive status at both ages [4]. An additional variable
representing cognitive change was constructed as a
change between cognition categories at ages 11 and 50.
The cognitive tests included at age 11, are widely used
and have been validated in several longitudinal studies:
reading comprehension: [55], maths test [56], copying designs test: [57], general ability test [54].
Social networks and support: questions on sources of
advice about important changes in life (age 33); whether

Page 4 of 15

they had someone to turn to for advice/support, and, if
so, who (ages 42, 50); a social network variable was
derived from the latter two questions (having someone
to turn to for advice/support, and who), equating to
whether anyone was available for advice/support, and
who that person was; having someone who would listen
to their problems; whether they visited/were visited/had
phone/mail contacts with friends in last 2 weeks (age
50); marital/partnership status (ages 33, 42, 50), household size (ages 33, 50), and had help or advice from
friends/neighbours/colleagues and family members (ages
33, 42, 50). Those in relationships were asked whether
they assessed their relationship as a happy one, and
ratings of ‘how happy’ (ages 33, 42, 50) (question type/

wording varied slightly by wave).
Health behaviour: questions on participation in sporting activities, and its frequency at ages 33, 42 and 50;
alcohol consumption and frequency at ages 33, 42 and
50; current smoking status, and frequency, at ages 42
and 50. Obesity was measured by body mass index at
ages 33 and 42. Physical health: Self-reported health
status at ages 33 and 50; reported fits/epilepsy at ages
33, 42 and 50; biomarkers and measurements at age 44,
including serum cholesterol, triglycerides, low density
lipoprotein, high density lipoprotein, blood pressure and
waist circumference. Mental health: psychiatric morbidity was measured with the Malaise Index (the 9-item
Malaise Inventory was analysed) [58] at ages 33, 42 and
50. This was developed from the Cornell Medical Index
(also referred to as mental well-being). Each positive
response to the nine items is scored as one, with a total
score ranging between 0 and 9, with higher scores indicating worse mental health. Additionally, the score was
dichotomised with scores of 4+ indicating poorer health.
Standard socio-demographic characteristics included
gender, marital/partnership status, highest level of qualification by age 50, housing tenure in childhood (ages 7
and 11); socio-economic position: life-course social class,
using the six standard Registrar General’s categories
(father’s social class, as reported by parents, at respondents’ birth, and at ages 7 and 11; respondent’s selfreported social class (at ages 42 and 50). At age 50,
current employment was included as an indicator of socioeconomic activity. The question wording of variables
included in the final model is given in Additional file 2.

Analyses
The distributions of variables were examined with univariate statistics; bivariate analyses were conducted to test
associations between independent and the dependent
variables. Variables which were significantly associated
with the dependent variable at least at the 0.05 level of

statistical confidence, or which were of border-line
significance, in bivariate analyses were included in fully


Bowling et al. BMC Psychology (2016) 4:58

adjusted, multi-variable analysis (see variables in Additional
file 3).
Multiple linear regression analysis was used to examine the independent influence of the independent variables on cognitive status at age 50. Hierarchical
regression was selected as the method of variable entry
as it is theory- rather than data-driven. No intercorrelation was higher than r = 0.40, indicating that
multicollinearity was at an acceptable level, permitting
variable entry.
Complete case and multiple imputation analysis were
conducted. Missing information was imputed by Multiple Imputation by Chained Equations for the final
model [59] to deal with reduced sample size over the
NCDS waves and boost analytical power. The uncertainty from estimating imputed values is accounted for
in standard error estimates. The method used was multiple imputation. The method was used for those who
were present in the study at ages 11 and 50. In this imputation all variables identified in the final model of
complete case analysis and additional variables predictive of missingness were included. Ten imputed datasets
were created. Data were analysed using STATA 13.0. It
should be noted that missing cases for the biomedical
variables appeared to be ‘not-at-random’ (possibly because of higher non-responses to items and nurse
follow-up interviews to collect these data) so biomedical
information could not be used in the imputed analyses
and were thus withdrawn from the analyses.

Results
Comparisons between original sample and analytical
sample presented here


The analytical sample included 49% males and 51%
females, compared to the ‘birth’ sample of 52% males
and 48% of females. There were no differences between
their distributions of highest achieved qualification nor
marital/partnership status (age 50). Slightly more rated
their health status as excellent in the analytical sample,
compared with the cross-sectional sample at age 33, but
the difference was small (1%); there were no differences
in self-reported health status at age 50. The mean (S.D.)
for the reading scores at age 11 was 16.7 (6.1) in the
analytical sample and 16.0 (6.3) in the cross-sectional
sample; for the maths scores these were 17.9 (10.2) and
16.6 (10.4); for the copying scores 8.4 (1.4) and 8.3 (1.5);
and for the General Ability Test 45.3 (15.5) and 42.9
(16.1) (all respectively). The mean (and S.D.) results for
the cognitive test scores at age 50 were almost identical
in both samples: letter accuracy: 4.40 (4.11) in the analytical sample and 4.42 (4.12) in the cross-sectional sample; animal naming: 22.32 (6.30) and 22.28 (6.30); word
recall immediate: 6.54 (1.49) and 6.54 (1.49); word recall
delayed: 5.41 (1.84) and 5.41 (1.84) (all respectively).

Page 5 of 15

Characteristics of the analytical sample

The sample characteristics are shown in Table 1 (and
see Additional file 3 referenced earlier). At age 11, 6% of
child respondents’ fathers were in professional social
classes, 19% managerial-technical, 10% skilled nonmanual and the remainder manual. At age 42, 6% of participants were classified as professional, 39% as
managerial-technical, 22% skilled nonmanual and the remainder manual. At age 50, 4% of

respondents reported having a Higher Degree/vocational
NVQ5 Diploma (National Vocational Qualifications
range from Level 1 focusing on basic work activities to
Level 5 for senior management), 31% had achieved a Degree/Teaching Diploma/vocational NVQ4 Diploma, 17%
had Advanced General Certificate of Secondary Education (AS/A-levels) or equivalent qualifications, 25% had
General Certificate of Secondary Education (GCSE) or
equivalent qualifications, 11% had Certificate of Secondary
Education (CSE) or equivalent qualifications; and 11% had
no qualifications.
The continuous distributions of all cognitive tests at
ages 11 and 50 were approximately normal. Categorically, at age 11, 28% of respondents were classified in the
‘below the mean’ category, 35% in the ‘mean’ category,
and 37% in the ‘above the mean’ category. At age 50, the
comparable percentages were 31, 39 and 30%
respectively. Cognitive score changes between ages 11
and 50 show that almost a third of the analytical sample’s cognitive scores deteriorated between ages 11 and
50 (with over 6% showing deterioration over two levels
(meaning scoring ‘above the mean’ at age 11 and scoring
‘below the mean’ at age 50) and 25% deteriorated by one
level (either from ‘above the mean’ at age 11 to ‘at the
mean’ at age 50, or from ‘at the mean’ at age 11 to ‘below
the mean’ at age 50). Under half of participants, 44%,
had unchanged scores (in the same category) at both
ages and a quarter achieved better results at age 50 (almost 20% improving by one category and almost 5% improving by 2 categories) (Additional file 4).
Most (83%) of respondents at age 33, and 64% at age
50, reported no participation in any civic organisation.
Participating in one civic organisation was reported by
14% of respondents at age 33 and by 25% at age 50.
Table 2 shows the crude bivariate associations between
standardized cognitive scores at age 50 and potential

predictive variables, as estimated by linear regression (at
least at 0.05 level, or achieving borderline significance).
Those with higher level achieved qualifications at age 50
had the strongest positive association with cognition at
age 50 (those respondents who reported having AS/Alevels/diploma/degree achieved 1.4 to 2.6 points higher
cognitive scores, compared to those with no qualification); those with good or excellent self-rated health at
age 33 had 0.7 to 1.0 higher cognitive scores; those who


Bowling et al. BMC Psychology (2016) 4:58

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Table 1 Description of the sample and variables used in the analysis
Number

Frequencies
(%)/Mean (S.D)

% missing

Standardized score at age 11

8448

0.44 (3.12)

7.4

Standardized score at age 50


8751

0.02 (2.41)

4.0

Cognition

Social network
Age 33
Has at least 1 friend/ neighbour/ colleague could turn
to for advice (Number of people showed only for
description; variable used as dichotomous)

Has at least 1 member of family could turn to for advice
(Number of people showed only for description; variable
used as dichotomous)

Number of civic group activities participated in (used as
continual variable; categories showed only for description)

7961
No/No one mentioned

12.7
56.3

Yes/Someone mentioned


43.7

0 people mentioned

56.3

1 person mentioned

28.2

2 people

12.2

3 people

2.7

4 people

0.3
7961

No/No one mentioned

12.7
8.7

Yes/Someone mentioned


91.3

0 people mentioned

8.7

1 person mentioned

21.8

2 people

25.5

3 people

28.2

4 people

15.8
7961

0.22 (0.52)

No activity

82.7

1 activity


13.8

2 activities

2.8

3+

0.7

12.7

Age 42
Has somebody could turn to for advice/support

8641
No

7.2
3.3

Yes, family member

75.0

Yes, friend/neighbour/colleague

21.7


Age 50
Number of civic group activities participated in (used as
continual variable; categories showed only for description)

9117

0.49 (0.80)

No activity

65.1

1 activity

24.6

2 activities

7.5

3+

2.8

0.1

Health and health behaviour
Age 33
Self-rated health


7843

14.0

Poor

1.3

Fair

11.1


Bowling et al. BMC Psychology (2016) 4:58

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Table 1 Description of the sample and variables used in the analysis (Continued)
Good

52.2

Excellent

35.4

Age 42
Mental well-being (Malaise score; 9-item version)

8412

Better (0–3)
Worse (4+)

Frequency of drinking alcohol

7.8
88.5
11.5

8459
Never

1.2

Not now/ special occasions

16.0

Once in a week

29.9

More often

52.9

Frequency of smoking

8464


7.2

No

46.1

Used to/ occasionally

30.2

Daily

23.7

Takes part in sporting activities and frequency

8455

7.3

Not regularly/less often than
once in month

27.7

2–3 times in month

6.5

Once in week


18.8

2–3 times in week

21.2

4 times in week/every day

25.8

Socio-economic background
Age 11
Father’s (male head) social class

8249

9.5

Professional

5.8

Managerial-technical

19.2

Skilled non-manual

9.5


Skilled manual

40.2

Partly skilled

15.8

Unskilled

5.0

No male head

4.5

Housing tenure

8342

8.6

Owner occupied

48.1

Council rented

39.8


Private rented

7.3

Rent free

4.8

Age 42
Own social class

7297

20.0

Professional

5.6

Managerial-technical

38.9

Skilled non-manual

21.6

Skilled manual


19.2

Partly skilled

11.9


Bowling et al. BMC Psychology (2016) 4:58

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Table 1 Description of the sample and variables used in the analysis (Continued)
Unskilled

2.8

Age 50
Highest achieved qualification

9113

0.1

None

11.0

CSE or equivalent

11.2


GCSE or equivalent

25.4

AS/A level or equivalent

17.2

Degree/teaching diploma/vocational
NVQ4 diploma

30.8

Higher Degree/vocational NVQ5 diploma

4.4

Other
Gender

9119

0

Male

49.0

Female


51.0

participated in civic group activities at ages 33 and 50
scored 0.4 to 0.6 more in cognitive tests; and those who
took part in sporting activities achieved between 0.4 to
0.6 higher cognitive scores. An inverse association was
found with father’s social class and own reported social
class (those whose fathers were in manual groups scored
1.2 to 1.7 lower, compared with those whose fathers
were in professional classes; those who reported themselves in manual classes at age 42 scored 1.7 to 2.1 lower).
Further bivariate regression analyses showed that each
individual type of civic activity at age 33 had a significant
and positive effect on cognitive status at age 50 (active
member of political party: B = 0.97, 95% CI 0.61 to 1.34,
p-value < 0.001; active in charity activities: 0.97; 0.81 to
1.15; <0.001; active in women’s organizations: 0.81; 0.46
to 1.16; <0.001; active in neighbourhood watch: 0.63;
0.29 to 0.96; <0.001; active in school/parental organisaton: 0.64; 0.40 to 0.88; 0.001). Although there were some
small differences between individual regression coefficients, confidence intervals substantially overlapped, and
differences between effects of different activities were
not statistically significant.
Multivariable analyses

Using the imputed dataset, multiple linear regression
analysis was conducted to assess the independent influence of those variables identified in bivariate analysis as
potential predictors. Table 3 shows the results of the
fully-adjusted model. Participation in civic organizations,
clubs or groups at ages 33 and 50 both retained significant associations with cognition at age of 50 (participation in each additional civic activity increased cognitive
scores by, on average, 0.12 points).

Support from family at age 33 was inversely associated
with cognition at age 50: having at least one family

member to whom respondent could turn to for advice at
age 33 was associated with a decreased cognitive score
at age 50 by 0.11 points. Support from friends at ages 33
and 42, respectively, did not retain statistical significance, as their influence was explained by the other variables included in the regression model.
Those who at age 33 reported their health as goodexcellent had slightly higher cognitive scores by 0.14–0.16
points at age 50, compared with those whose self-reported
health was poor (reference category) This was not statistically significant and, as Table 3 shows, the 95% confidence
interval was wide, ranging from -0.36 to 0.64. Those who
registered 4 or more on the Malaise Index (indicating worse
mental well-being) at age 42 had on average 0.18 lower cognitive score at age 50 than those who scored 0–3.
The association of participation in sport (and frequency) at age 42 with later cognitive outcomes showed
a positive effect for those who participated in sport at
least weekly. The latter had 0.15–0.19 higher overall
cognitive scores compared with those who participated
in sport less often or not at all. Associations between
frequency of drinking alcohol, smoking cigarettes and
cognitive scores at age 50 were fully explained by other
variables in the final model.
The effect of socio-economic characteristics in childhood (father’s socio-economic position and housing tenure at age 11) was fully explained in the final model.
Own social class at age 42 was negatively significantly
associated with cognition at age 50, and those in manual
social classes (skilled, partly skilled, unskilled) had 0.29–
0.38 points lower overall cognitive scores compared with
those in non-manual classes. Higher cognitive scores at
age 50 were achieved by those with higher-level qualifications (showing stepwise increase by 0.23 to 1.08 points
compare to those who did not achieved any qualification).



Bowling et al. BMC Psychology (2016) 4:58

Page 9 of 15

Table 2 Bivariate associations between standardized cognitive score at age 50 and predictive variables over the life-course
(linear regression)
Unstandardized B 95% CI (p-value) t-test
Standardized β
Cognition in childhood
Standardized score at age 11

Per unit

0.29
0.36

0.27 to 0.30
(<0.0001)

35.0

Social network
Age 33
Has at least 1 friend/ neighbour/colleague
could turn to for advice

No / No one mentioned

Has at least 1 member of family could turn

to for advice

No/ No one mentioned

Yes

Yes

0 (ref)
0.41
0.08

0.30 to 0.51
(<0.0001)

7.70

0 (ref)
0.46
0.08

0.34 to 0.58
(<0.0001)

7.50

0.56
0.12

0.45 to 0.65

(<0.0001)

11.00

Family member

0.60
0.11

0.31 to 0.90
(<0.0001)

4.01

Friend/colleague/neighbour

0.64
0.11

0.33 to 0.95
(<0.0001)

4.03

Per 1 activity +

0.39
0.13

0.32 to 0.45

(<0.0001)

12.13

Number of civic group activities participated in Per 1 activity +
Age 42
Has somebody could turn to for advice

No

0 (ref)

Age 50
Number of civic group activities participated in
Health and Health behaviour
Age 33
Self-rated health

Poor

0 (ref)

Fair

0.23
0.03

–0.28 to 0.75
(0.38)


0.88

Good

0.73
0.15

0.24 to 1.22
(0.004)

2.91

Excellent

0.98
0.20

0.41 to 1.48
(<0.0001)

3.87

Age 42
Mental well-being (Malaise score;
9-item version)

Frequency of drinking alcohol

Frequency of smoking


Better (0–3)

0 (ref)
–0.45
–0.06

–0.61 to –0.29
(<0.0001)

Not now/ special occasions

–0.09
–0.1

–0.60 to 0.41
(0.72)

–0.35

Once in a week

0.11
0.02

–0.38 to 0.61
(0.66)

0.44

More often


0.56
0.11

0.07 to 1.06
(0.03)

2.23

Worse (4+)
Never

–5.41

0 (ref)

No

0 (ref)

Used to / occasionally

–0.08
–0.01

–0.20 to 0.04
(0.21)

–1.24


Daily

–0.58
–0.10

–0.71 to –0.44
(<0.0001)

–8.63


Bowling et al. BMC Psychology (2016) 4:58

Page 10 of 15

Table 2 Bivariate associations between standardized cognitive score at age 50 and predictive variables over the life-course
(linear regression) (Continued)
Takes part in sporting activities
and how frequently

Not regularly/ less often than once in month

0 (ref)

2–3 times in month

0.51
0.05

0.28 to 0.74

(<0.0001)

4.46

Once in week

0.52
0.08

0.36 to 0.67
(<0.0001)

6.55

2–3 times in a week

0.58
0.10

0.43 to 0.73
(<0.0001)

7.61

4 times in week/every day

0.40
0.07

0.25 to 0.54

(<0.0001)

5.43

Managerial-technical

–0.40
–0.07

–0.64 to –0.15
(0.002)

–3.17

Skilled non-man

–0.65
–0.08

–0.93 to –0.38
(<0.0001)

–4.68

Skilled manual

–1.15
–0.23

–1.38 to –0.92

(<0.0001)

–9.75

Partly skilled

–1.35
–0.20

–1.60 to –1.09
(<0.0001)

–10.46

Unskilled

–1.66
–0.15

–1.98 to –1.34
(<0.0001)

–10.25

No male head

–1.04
–0.09

–1.37 to –0.71

(<0.0001)

–6.23

Council rented

–0.75
–0.15

–0.86 to –0.64
(<0.0001)

–13.12

Private rented

–0.48
–0.05

–0.69 to –0.28
(<0.0001)

–4.60

Rent free

–0.31
–0.03

–0.56 to –0.06

(0.02)

–2.42

Managerial-technical

–0.51
–0.10

–0.75 to –0.26
(<0.0001)

–4.09

Skilled non-man

–0.96
–0.17

–1.21 to –0.70
(<0.0001)

–7.34

Skilled manual

–1.71
–0.28

–1.97 to –1.45

(<0.0001)

–12.97

Partly skilled

–1.55
–0.21

–1.83 to –1.28
(<0.0001)

–11.04

Unskilled

–2.11
–0.15

–2.50 to –1.71
(<0.0001)

CSE or equivalent

0.41
0.05

0.20 to 0.62
(<0.0001)


3.89

GCSE or equivalent

0.96
0.17

0.78 to 1.14
(<0.0001)

10.56

AS/A level or equivalent

1.37
0.22

1.18 to 1.56
(<0.0001)

14.24

Socio-economic background
Age 11
Father’s social class

Housing tenure

Professional


0 (ref)

Owner occupied

0 (ref)

Age 42
Own social class

Professional

0 (ref)

–10.43

Age 50
Highest achieved qualification

None

0 (ref)


Bowling et al. BMC Psychology (2016) 4:58

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Table 2 Bivariate associations between standardized cognitive score at age 50 and predictive variables over the life-course
(linear regression) (Continued)


Gender

Degree/teaching diploma/vocational NVQ4 diploma

2.07
0.40

1.89 to 2.24
(<0.0001)

23.46

Higher Degree/vocational NVQ5 diploma

2.63
0.22

2.35 to 2.91
(<0.0001)

18.62

0.47
0.10

0.37 to 0.57
(<0.0001)

Male
Female


Females had, on average, 0.49 points higher cognitive
scores than males.
In summary, the model shows that cognitive status at
age 11, participation in civic activities (ages 33 and 50),
frequent participation in sport (age 42), having higher
level qualifications by age 50, and female gender were
positively and significantly associated with cognitive outcomes at age of 50. Having a father in manual socioeconomic groups at age of 11, reporting oneself to be in
a manual group, and higher Malaise Index scores (age
42) were negatively associated with cognitive outcomes
at age of 50. Multiple regression analysis, with age 50
cognitive status as the dependent variable, showed that
the overall model was highly significant, and explained
approximately 18% of the variance in cognitive scores
at age 50.

Discussion
This study investigated associations between life-course
social engagement (civic participation) and cognitive
status at age 50, adjusting for potential confounders.
Our approach aimed to be original by using a large,
British longitudinal birth cohort (NCDS), which enabled
us to take into account complex interactions between
social and biological processes, thus employing a life
course perspective at multiple time-points. It was pointed
out in the Background that a small number of surveys
have indicated that social integration, engagement and
participation, and having strong networks are associated
with better cognitive outcomes, although relatively few
studies have used life course data.

Age 11 was selected for analysis because the range of
cognitive tests was wider, and inclusion of age 11 rather
than age 16 optimised the sample size for analysis. Ages
33, 42 and 50 were selected for analysis because these
were the principal adult survey sweeps of NCDS, and
questions were included which measured the variables
of interest. Fully-adjusted analyses showed that those
variables which were positively and significantly associated with cognitive status at age 50 were: cognitive ability at age 11, participation in civic activities (including
clubs, groups) (ages 33, 50), frequent engagement in
sport (age 42), better (i.e. lower) Malaise Index scores
(age 42), having higher level qualifications, and female

0 (ref)
9.08

gender. Socio-economic indicators at ages 11 and 42
were inversely associated with cognitive status at age 50.
The proportion of explained variance in the regression
model (18%), while modest, is relatively impressive given
the multi-faceted causality of cognitive ability. Thus the
results reported here indicated modest longitudinal
associations between adult social engagement and cognitive function at age 50, which persisted after adjusting
for covariates. The implication is that if people continue to engage throughout life, maintaining related social skills, there may be some protection from cognitive
decline.
Despite the literature indicating the importance of
having strong social networks and support for optimum
mental and physical health outcomes, and for reducing
mortality risk [24, 26, 60], it is uncertain why support
from family, but not from friends, was inversely associated with cognitive scores in this study. It would be
expected from this literature that support from family at

least would be positively associated with physical and
mental health outcomes, especially as relatives are more
likely than friends to provide instrumental and informational support [61, 62].
The strength of the study was its longitudinal nature,
based on a large, national British cohort of males and
females: the National Child Development Study. A
limitation of the study was that only memory and
executive function were tested, their measurement was
partial, and the measures were non-conventional
neuropsychological tests. As with any longitudinal
study, and despite excellent initial response rates, differential patterns of response in NCDS over the life
course may lead to a danger of attrition bias in a
complete-case analysis [63, 64]. In the 39 years
between the cognitive tests at age 11 and age 50 we
would, for example, expect to lose a slightly disproportionate number of men, those from lower SES backgrounds, those with less good health and those with
lower cognitive skills/qualifications. To correct for this,
a process of multiple imputation by chained equations
(MICE) was employed. The plausibility of the Missing
at Random assumption was maximised [65], and the
imputation process was in line with its assumptions
[66]. Thus the imputed population at age 50 had the


Bowling et al. BMC Psychology (2016) 4:58

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Table 3 Multiple linear regression; association between predictors and cognitive status at age 50 (data imputed for missing cases;
estimated model)
Stand-ard-ised β


95% CI (p-value)

t-test

Cognition in childhood
Standardized score at age 11

Per unit

0.19

0.17 to 0.21 (<0.0001)

18.73

Has > = 1 friend/neighbour/ colleague
could turn to for advice (ref = 0 ‘No/No
one mentioned)

Yes

0.10

–0.04 to 0.17 (0.16)

1.42

Has at least 1 member of family
could turn to for advice (ref = 0 ‘No/No

one mentioned)

Yes

–0.11

–0.18 to 0.08 (0.02)

–2.32

Number of civic group activities
participated in

Per 1 activity +

0.12

0.02 to 0.22 (0.03)

2.22

Family member

–0.01

–0.30 to 0.27 (0.93)

–0.09

Friend/colleague/neighbour


0.02

–0.27 to 0.32 (0.89)

0.15

Per 1 activity more

0.13

0.07 to 0.20 (<0.0001)

4.34

Fair

0.01

–0.49 to 0.50 (0.99)

0.01

Good

0.14

–0.36 to 0.64 (0.58)

0.56


Excellent

0.16

–0.35 to 0.68 (0.52)

0.64

Mental well-being (Malaise
score; 9-item version)
ref = 0 ‘Better 0/3’

Worse (4+)

–0.18

–0.33 to –0.02 (0.03)

–2.23

Frequency of drinking
alcohol (ref = 0 ‘Never’)

Not now/Special occasions

0.11

–0.39 to 0.60 (0.67)


0.42

Once a week

0.18

–0.32 to 0.67 (0.49)

0.71

More often

0.30

–0.21 to 0.81 (0.24)

1.18

Used to/ occasionally

0.07

–0.04 to 0.19 (0.21)

1.22

Daily

0.03


–0.1 to 0.17 (0.61)

0.51

2–3 times a month

0.16

–0.05 to 0.38 (0.13)

1.50

Once a week

0.15

0.01 to 0.30 (0.03)

2.14

2–3 times a week

0.19

0.05 to 0.33 (0.01)

2.59

4 times week / everyday


0.18

0.05 to 0.31 (0.006)

2.73

Managerial-technical

–0.17

–0.42 to 0.07 (0.16)

–1.41

Skilled non-manual

–0.24

–0.53 to 0.05 (0.09)

–1.70

Skilled manual

–0.22

–0.47 to 0.01 (0.05)

–1.94


Partly skilled

–0.27

–0.52 to –0.03 (0.03)

–2.21

Unskilled

–0.29

–0.58 to 0.02 (0.06)

–1.90

No male head

–0.10

–0.41 to 0.24 (0.57)

–0.57

Council rented

–0.03

–0.15 to 0.08 (0.51)


–0.66

Private rented

0.02

–0.17 to 0.22 (0.82)

0.23

Social network
Age 33

Age 42
Has somebody could turn to for
advice (ref = 0 ‘Nobody’)
Age 50
Number of civic group activities
participated in
Health and Health behaviour
Age 33
Self-rated health (ref = 0 ‘Poor’)

Age 42

Frequency of smoking (ref = 0 ‘No)
Takes part in sporting activities
and how frequently
(ref = 0 ‘Not regularly’)


Socio-economic background
Age 11
Father’s (male head) social
class (ref = 0 ‘Professional’)

\Housing tenure (ref = 0
‘Owner occupied’)


Bowling et al. BMC Psychology (2016) 4:58

Page 13 of 15

Table 3 Multiple linear regression; association between predictors and cognitive status at age 50 (data imputed for missing cases;
estimated model) (Continued)
Rent free

0.06

–0.21 to 0.25 (0.63)

0.48

Managerial-technical

–0.06

–0.28 to 0.16 (0.61)

–0.50


Skilled non-manual

–0.17

–0.43 to 0.09 (0.19)

–1.27

Skilled manual

–0.28

–0.54 to –0.03 (0.03)

–2.17

Partly skilled

–0.32

–0.60 to –0.04 (0.02)

–2.22

Unskilled

–0.38

–0.76 to –0.004 (0.05)


–1.96

CSE or equivalent

0.23

0.003 to 0.41 (0.03)

2.16

GCSE or equivalent

0.33

0.15 to 0.51 (<0.0001)

3.61

AS/ A-level or equivalent

0.61

0.42 to 0.80 (<0.0001)

6.27

Degree/teaching diploma/vocational
NVQ4 diploma


0.83

0.64 to 1.02 (<0.0001)

8.44

Higher degree/NVQ5 diploma

1.08

0.79 to 1.38 (<0.0001)

7.17

Age 42
Own social class
((ref = 0 ‘Professional’)

Age 50
Highest achieved qualification
(ref = 0 ‘None’)

Gender (ref = 0 Male)

Female

Constant

0.49


0.38 to 0.60 (<0.0001)

9.22

–0.90

–1.66 to –0.14 (0.02)

–2.32

R2

0.1767

Adjusted R2

0.1730

F statistic (40, 6463.0)

42.47
<0.0001

P-value

same basic characteristics as those at age 11 (e.g. the
analytical sample did not ‘lose’ more people with lower
(age 11) cognitive powers in the intervening years than
those with higher cognitive powers).


Conclusions
In conclusion, this paper contributes to the body of
literature on potential behavioural risk factors for cognitive decline [22, 67], and on the benefits of civic participation. Adult social engagement through civic activities
could potentially maintain cognitive function at age 50,
independently of behavioural and socio-economic circumstances, supporting theories of neuroplasticity. The
direction of causality can, of course, be questioned. In
the study reported here, cognitive status was assessed at
age 11 (educational assessments), then not again until
age 50 (cognitive function survey measures). The analyses controlled for potential confounding variables,
including physical, biomedical, and mental health variables; only the Malaise Index was a significant predictor.
It is possible that the sample was too young at age 50
for the full assessment of their long-term impact on cognitive status, or that the physical health variables were
insufficiently sensitive. The findings require verification
in future longitudinal surveys, using robust measures,
and with the pertinent measures repeated at key waves.
While the limitations of this study preclude definitive
conclusions, there is a case for causal interpretation of

this association. There is a rich literature on how social
factors might improve physical and psychological health
and well-being, both directly and as stress buffers, for
example via social comparisons of oneself with others’,
perceptions of self-esteem and sense of control over life
and identity [68]. Potential causal mechanisms in relation to cognitive function include the stimulation
derived from social interaction and participation, with
maintenance of social and communication skills, which
might preserve cognitive function. In conclusion, potential
modifiable targets for public health policy intervention in
promoting cognitive health include encouragement of civic
engagement and provision of opportunities for this, and

modification of behavioural risk factors (encouragement of
physical activity).

Additional files
Additional file 1: Table S1. Response by wave. National Child
Development Study (NCDS) longitudinal response by follow-up sweep
number. Response rates to NCDS survey sweeps. (DOCX 19 kb)
Additional file 2: Table S2. Variable description. Question wording of
variables included in the final model. (DOCX 23 kb)
Additional file 3: Table S3. Sample description bivariates. Description
and bivariate association from linear regression between each theorydriven independent variable considered for entry and cognition at age
50. Description and bivariate association from linear regression analyses.


Bowling et al. BMC Psychology (2016) 4:58

Additional file 4: Table S4. Change tables cognition. Distribution of
categorised cognitive scores. Cognitive score changes between ages 11
and 50. (DOCX 18 kb)
Abbreviations
ESRC: Economic and Social Research Council; NCDS: National Child
Development Study; NVQ: National Vocational Qualifications (from Level
1 on basic work activities, to Level 5 senior management)
Acknowledgement
The NCDS data set is held on the Data Archive at the University of Essex
/>Those who carried out the original NCDS data collection and analyses hold
no responsibility for the further analyses and interpretations presented here.
The authors thank Professors Martin Knapp and Emily Grundy at LSE for their
helpful comments and support throughout.
Funding

This study formed a work-package within the wider MODEM protocol (modelling outcome and cost impacts of interventions for dementia), funded by
the Economic and Social Research Council (ESRC award no. ES/L001896/1),
and led by Professor Martin Knapp at LSE: gtr.rcuk.ac.uk/project/2ADEAE65E884-4B74-9283-31AD7B235014 . The funders of the study had no role in
the study design, data collection, analysis, interpretation of data, writing
up or publication.
Availability of data and materials
The datasets analysed during the current study are available on registration
at the Data Archive at the University of Essex: https://
discover.ukdataservice.ac.uk/series/?sn=2000032
Authors’ contributions
AB conceived the idea for the study, its objectives and design, reviewed the
literature, prepared and wrote the final draft of this paper. JP, with AB and
BD, identified comparable cohort wave variables; JP undertook the data
merges, statistical analyses, data imputation, prepared the tables and variable
lists, wrote the initial draft of the methods and results, contributed to and
reviewed the final version of the manuscript. BD provided support for the
NCDS dataset throughout, collaborated on interpretation of data and writing
up, contributed to and reviewed the final version of the manuscript. AB, JP
and BD collaborated in planning the analyses. All authors had access to the
NCDS dataset, which is accessible at the Data Archive: https://
discover.ukdataservice.ac.uk/series/?sn=2000032. AB is the guarantor and
accepts full responsibility for the conduct of the study, and controlled the
decision to publish. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Multicentre Research Ethics Committee (MREC) approval was sought for
NCDS follow-ups from 2000 on, and for the Biomedical Survey. The 1958 and

1965 follow-ups pre-dated the establishment of ethics committees; the 1969,
1974, 1981 and 1991 follow-ups came before the establishment of the MREC
system. Internal ethical reviews were undertaken for these waves. Participants in
later waves were required to sign informed consent, and ethical approval was
obtained from South East and London Multicentre Research Ethics Committee.
(Shepherd, P.M. An Introduction to the Background to the Study and Methods
of Data Collection in The National Child Development Study. Social Statistics
Research Unit. London City University 1985). NCDS sought informed parental
consent for the 7-year (1965), 11-year (1969) and 16-year (1974) surveys.
Author details
1
Faculty of Health Sciences, University of Southampton, Highfield Campus,
Southampton SO171BJ, UK. 2Brunel University London, College of Health and
Life Sciences, Department of Clinical Sciences, London, UK. 3Centre for

Page 14 of 15

Longitudinal Studies, UCL Institute of Education, 20 Bedford Way, London
WC1H 0AL20, UK.
Received: 10 August 2016 Accepted: 3 November 2016

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